Abstract
We consider tomographic imaging problems where the goal is to obtain both a reconstructed image and a corresponding segmentation. A classical approach is to first reconstruct and then segment the image; more recent approaches use a discrete tomography approach where reconstruction and segmentation are combined to produce a reconstruction that is identical to the segmentation. We consider instead a hybrid approach that simultaneously produces both a reconstructed image and segmentation. We incorporate priors about the desired classes of the segmentation through a Hidden Markov Measure Field Model, and we impose a regularization term for the spatial variation of the classes across neighbouring pixels. We also present an efficient implementation of our algorithm based on state-of-the-art numerical optimization algorithms. Simulation experiments with artificial and real data demonstrate that our combined approach can produce better results than the classical two-step approach.
Original language | English |
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Journal | Inverse Problems in Science and Engineering |
Volume | 24 |
Issue number | 8 |
Pages (from-to) | 1432-1453 |
ISSN | 1741-5977 |
DOIs | |
Publication status | Published - 2015 |
Keywords
- Tomographic reconstruction
- Segmentation
- Regularization
- Numerical optimization
- Hidden Markov Measure Field Models